Computer-implemented method to screen for longitudinal-seam anomalies

ABSTRACT

Embodiments of the present invention provide computer-implemented methods to detect crack-like features in pipeline welds using magnetic flux leakage data and pattern recognition. A screening process, for example, does not affect or change how survey data is recorded in survey tools; only how it is analyzed after the survey data is completed. Embodiments of the present invention can be used to screen for very narrow axial anomalies in the pipeline welds, and may also be used to predict the length of such anomalies. Embodiments of the present invention also produce a listing of the anomalies based on their relative signal characteristics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation and claims priority and benefit toU.S. Ser. No. 12/270,432, filed on Nov. 13, 2008, which claims priorityand benefit to U.S. Provisional Application Ser. No. 61/008,822, filedDec. 21, 2007, and both of which are hereby incorporated herein byreference in their entireties. This application also is a continuationof and claims priority and benefit to U.S. Ser. Nos. 12/949,896 and12/950,118 each filed on Nov. 19, 2010, and which likewise each claimpriority to U.S. Ser. No. 12/279,432 and U.S. Provisional ApplicationSer. No. 61/008,822, and all of which are hereby incorporated herein byreference in their entireties.

BACKGROUND

1. Field of Invention

The present invention relates to the detection of cracks and incompletefusions in pipeline welds and, more particularly, to a screening processwhich utilizes pattern recognition to identify the location of cracksand incomplete fusions along a welded pipeline using transverse magneticflux technology.

2. Description of Related Art

Pipelines with welded longitudinal-seams have experienced in-servicefailures due to incomplete fusion and hook cracks. These issues are wellknown, well understood, and have been serious considerations of pipelineintegrity management groups throughout the industry for a number ofyears. They have also been one of the focus elements of the regulatoryagencies responsible for assuring that pipeline operators are assessingall threats to the integrity of their pipelines.

In normal analysis processes utilizing transverse flux technology,detection processes have been primarily focused on the identificationand quantification of volumetric metal loss anomalies along a pipeline.These algorithms utilize the amount of flux leakage detected, the lengthof anomaly and width of anomaly (number of channels) to determine depth.When a “metal-loss” sizing algorithm is applied to narrow axialanomalies, the resulting predicted depth can be considerably shallowerthan the actual depth. Because there are a limited number of channelsaffected by the anomalies, the calculated depth is low and most oftenbelow a minimum reporting threshold. This results in a non-reportedanomaly, which could lead to pipeline failures. As such, recognized byApplicants is the need for a new identification process that overcomessuch limitations.

SUMMARY OF INVENTION

In view of the foregoing, embodiments of the present invention provide anew identification process developed through utilization of transverseflux inspection technology taken to a new level of sophistication with adisciplined methodical evaluation of anomaly signals. Particularly,embodiments of the present invention provide supplemental screeningprocesses applied to pipeline survey data, which utilize a transversemagnetic flux leakage method and pattern recognition to identifypotential longitudinal-seam anomalies in welded pipe, specificallyfocusing on the detection of incomplete fusion and hook cracks forexample. The screening process of embodiments of the present inventiondoes not affect or change how the survey data is recorded in the in-lineinspection (“ILI”) survey tools; only how it is analyzed after thesurvey data is completed. The flux leakage method is primarilyinfluenced by anomaly air gap, which is a function of anomaly length anddepth, steel properties, and hoop stress. Embodiments of the presentinvention, for example, can be used to screen for very narrow axialcrack-like anomalies in the pipeline longitudinal welds, and may also beused to estimate the length of such anomalies. Embodiments of thepresent invention also produce a characterization and anomaly evaluationlist based on their relative signal characteristics.

Embodiments of the present invention, for example, specifically identifyelectric resistance weld (“ERW”) anomalies using a pattern recognitionprocess. The application of this process can be sensitive to thepipeline steel properties, the capabilities of the specific ILI toolemployed, and the pipeline operating conditions under which the surveywas run. Thus, when applying embodiments of the present invention toother conditions, each variable can be considered for applicationspecific adjustment if desired.

Embodiments of systems, program product, and methods of the presentinvention, as applied to this complex anomaly discrimination, preferablyrequire confirmation and validation of the process applicability in eachcase. The confirmation will minimally consist of several validationexcavations utilizing “highest level” non-destructive evaluation (“NDE”)methods and, in some cases, will require removal of appropriate samplesfor destructive metallurgical evaluation in a laboratory.

Embodiments of the present invention also are designed to identifycertain potentially injurious anomalies such as hook cracks andincomplete fusion in, and immediately adjacent to, the longitudinal weldin pipeline. An embodiment of a method of process, for example, shouldbegin by determining the critical flaw dimensions to be used todetermine the maximum length of an anomaly in the magnetic flux leakagedata that will be considered during the review of a linear anomaly usingconventional critical flaw analysis equations. Examples of suchequations include, but are not limited to, engineering basedcalculations such as Corlas and Keifner. Prior to the review beginning,the data then should be reviewed to determine C-Scan (a 3-D colorcontour plot utilizing a few of the pipeline from a top plane view) andA-Scan (plot of trace signals from individual magnetic flux sensors)display settings for optimum identification of linear anomalies. Thedata then can be visually or electronically scanned, joint by joint,along the weld utilizing the C-Scan display to identify signal patternsgenerally consistent with longitudinal-seam anomalies. This, forexample, can be defined as a level one (or Level 1) candidate screen.

Once the data pattern is deemed to be a level one (or Level 1) candidateanomaly, an embodiment of the method or process utilizes one or moresaved C-Scan display settings as previously established and zooms (orenlarges a visual display) as necessary to analyze anomalies identifiedfor pattern confirmation. This, for example, can be defined as a leveltwo (or Level 2) candidate screen. If the pattern is deemed to be alevel two candidate anomaly, an embodiment of a method or process thengoes on to determine the approximate length of the anomaly and compareit to the maximum allowed for classification in this pipeline section,assuring that it does not exceed critical length. This, for example, canbe defined as a level three (or Level 3) candidate screen. If theanomaly is determined to be below the critical flaw length, it qualifiesas a level 3 candidate and can be documented in a table, chart, orsummary spreadsheet, for example.

Note that the critical flaw length for each pipeline segment underreview can be selected according to standard calculations consideringactual pipeline operating conditions and pipeline material properties,to establish the maximum length of anomalies for consideration aspotential axial crack-like defects located within the weld. That is fora pipeline operating, for example, at pressures substantially less thanstandard, an equivalent critical length can be established using actualoperating conditions. Beneficially, such selection aids indifferentiating the crack-like defects from trim issues. Beneficially,such selection aids in differentiating the crack-like defects from trimissues.

If the pattern is deemed to be a level three candidate anomaly, anembodiment of a method goes on to analyze the anomaly using an A-Scandisplay, zoom as necessary and manipulate the gain to enhance the signalpattern. This, for example, can be defined as a level four (or Level 4)candidate screen. If the pattern is deemed to be a level four candidateanomaly, a table, chart, or summary spreadsheet, for example, of theresults is generated for further analysis and final confirmation. This,for example, can be defined as a level five (or level 5) candidatescreen. Thereafter, embodiment of the present invention generates avalidation dig list for a representative sample of all confirmed levelfive Candidates for excavation, extensive NDE analysis, and possibleremoval for laboratory analysis. In view of the foregoing, embodimentsof the present invention provide a screening processes developed whichutilize transverse magnetic flux technology to a new level ofsophistication and detail.

BRIEF DESCRIPTION OF DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. Some of the features and benefits of the presentinvention having been stated, others will become apparent as thedescription proceeds when taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a flow chart illustrating a method for detectinglongitudinal-seam anomalies according to an embodiment of the presentinvention;

FIG. 2 a is a flow chart illustrating a method for detectinglongitudinal-seam anomalies according to an embodiment of the presentinvention;

FIG. 2 b is a perspective view of a C-Scan computer display showing themagnetic flux leakage data according to an embodiment of the presentinvention—the x-axis corresponds to length and the y-axis corresponds too'clock position;

FIG. 2 c-2 f are perspective views of a display of a computer showingthe optimal display settings used to detect longitudinal-seam anomaliesaccording to an embodiment of the present invention;

FIG. 3 is a perspective view of a C-Scan computer display of magneticflux leakage data having blooms associated therewith according to anembodiment of the present invention—the x-axis corresponds to distance,while the y-axis corresponds to the o'clock position;

FIG. 4 is a perspective view of a C-Scan display showing how the lengthof the displayed magnetic flux leakage data is measured according to anembodiment of the present invention;

FIG. 5 is a graphical view of an A-Scan smooth waveform having threeanomaly channels corresponding to a level four candidate anomalyaccording to an embodiment of the present invention—the x-axiscorresponds to distance while the y-axis corresponds to amplitude;

FIGS. 6 and 7 are graphical views of a ragged and uneven A-Scan waveformindicative of trim issues according to an embodiment of the presentinvention—the x-axis corresponds to distance while the y-axiscorresponds to amplitude;

FIG. 8 is a colored illustration of the display of the computerillustrated in FIG. 2 d showing a field for selecting a certain lengthof pipe along with a corresponding saved C-scan of the length of pipeaccording to an embodiment of the present invention;

FIG. 9 is a colored illustration of the magnetic flux leakage dataillustrated in FIG. 3 showing the color contrast of the T-MFL displayused to detect a candidate anomaly according to an embodiment of thepresent invention;

FIG. 10 is a colored illustration of the magnetic flux leakage dataillustrated in FIG. 2 b showing a strong contrast between T-MFL data andthe surrounding background of the display and illustrating an anomalysignal having red and yellow contrasting colors with a “bloom” risingabove and below the primary T-MFL signal according to an embodiment ofthe present invention;

FIG. 11 is a colored illustration of the magnetic flux leakage dataillustrated in FIG. 4 showing the color contrast of the T-MFL displayused to size the displayed anomaly and size the displayed imageaccording to an embodiment of the present invention;

FIG. 12 is a colored illustration providing an example of incorrectdisplay settings resulting in a poor, fuzzy display with no clearindication of a “bloom” extending radially above and below a potentialanomaly according to an embodiment of the present invention;

FIG. 13 is a colored illustration providing an example of optimumdisplay settings resulting in a clear and distinct contrasting signalwith a “bloom” of the signal extending radially above and below aclearly distinctive anomaly according to an embodiment of the presentinvention;

FIG. 14 is a colored illustration showing the extent of the “bloom” ofan anomaly signal extending into adjacent channels according to anembodiment of the present invention;

FIG. 15 is a colored illustration showing a high contrast anomaly signalof a crack-like anomaly in the presence of trend issues illustrated asyellow lines extending longitudinally on either side of the anomalyaccording to an embodiment of the present invention;

FIG. 16 is a colored illustration providing examples of an anomalysignal pattern characterized by the lack of an anomaly “bloom,”excessive length, and lack of symmetry indicative of trim issuesaccording to an embodiment of the present invention;

FIG. 17 is a colored illustration providing an example of an anomalysignal pattern highly indicative of a crack-like anomaly according to anembodiment of the present invention; and

FIG. 18 is a colored illustration providing examples of anomaly signalsfor metal loss or other volumetric anomalies that extend across(transverse to) the seam of the longitudinal weld according to anembodiment of the present invention.

DETAILED DESCRIPTION OF INVENTION

The present invention will now be described more fully hereinafter withreference to the accompanying drawings in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theillustrated embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.Like numbers refer to like elements throughout.

An embodiment of the present invention, for example, can include asupplemental screening process applied to survey data utilizing displaysoftware such as, for example, the Rosen Rosoft Display Softwaremanufactured by the ROSEN Swiss AG of Stans, Switzerland (such as, forexample, versions 6.60), to identify potential longitudinal-seamanomalies in welded pipelines, specifically focusing on the issues ofincomplete fusion and hook cracks. Although the present disclosuredescribes the present invention in conjunction with the Rosoft DisplaySoftware only, other forms of display software can be utilized as wellas understood by those skilled in the art.

An embodiment of a method of the present invention, such as shown inFIG. 1, can begin by collecting the transverse magnetic flux leakagedata (“T-MFL”) data from a survey tool (step 101), such as, for example,an ILI inspection tool. The T-MFL data is primarily influenced byanomaly air gaps, which are a function of anomaly length and depth,steel properties, and hoop stress. Once the T-MFL data has beencollected from the survey tool at step 101, it is transmitted to acomputer having a processor (not shown), as understood by those skilledin the art, for analysis. Such a transmission can be achieved via anynumber of wired or wireless communications techniques. At step 103, theprocessor then causes the T-MFL data to be displayed on a display as oneor more patterns of data representing signal characteristics of thepipeline weld. As will be discussed later, these signal characteristicscan be displayed as, for example, smooth waveforms, contrasting colors,erratic/non-erratic patterns, or symmetrical patterns.

At step 105, the processor will begin analyzing the T-MFL data basedupon its respective signal characteristics and critical flaw dimensions,both of which are used to determine whether the data represents apotential crack-like defect in the pipeline weld. The critical flawdimensions are a maximum length of an anomaly, which is represented bythe T-MFL data being displayed on the display, to be considered as apotential crack-like defect in the weld.

The determination of the critical flaw dimensions will now be discussed.As stated above, embodiments of the present invention can focus on thecharacterization of signals that may prove to be injurious crack-likedefects in a longitudinal weld. The transverse magnetic flux leakage(“T-MFL”) inspection produces anomaly signatures from crack-like defectsand from trim issues that can sometimes have very similar signalpatterns. Critical flaw sizes for anomalies are determine for eachpipeline segment under review using industry standard calculationsconsidering actual pipeline operating conditions and pipeline steelmaterial properties. Anomaly signals that have lengths substantiallylonger than the critical size are generally considered the result ofnon-injurious metal edges associated with trimming of the long seam. Theindustry standard calculations, for example, can establish the maximumlength of anomalies for consideration as potential narrow axialcrack-like defects located within a weld and can be used to filter outlong anomalies that would have failed due to hoop stress, for example.

More specifically, or example, a conservative minimum detection limitfor the transverse T-MFL tool is approximately 2″ long and 60% wallthickness penetration. For example, as understood by those skilled inthe art, anomalies with lengths greater than 150% of the calculatedcritical length (for a 60% through-wall flaw) may not be considered aspotential defects since they would have failed previously under normalpipeline operating conditions at or near 72% of its specific minimumyield strength (“SMYS”). As understood by those skilled in the art,these percentages or lengths can be adjusted or varied by anoperator/user or by a program controller to obtain any desired result.In the event that the pipeline is operating at pressures substantiallyless than 72% of SMYS, an equivalent critical length can be establishedas discussed above using actual operating conditions. The value suppliedfor length determination during the analysis screening process is 150%of the calculated critical length with no changes to the value beingnecessary. Other references to the “critical length” in this disclosure,by way of example, are the 150% value. Other values, however, may beused according to desired parameters.

Once the T-MFL data has been analyzed by the appropriate ILI vendor anda summary report/spreadsheet generated, a separate secondary analysiscan be performed based on the signal characteristics and critical flawdimensions to thereby develop a list of potential anomalies (step 107).Such an output can typically be a summary spreadsheet, associated screencaptures and dig location sheets. Thereafter the process may end or berefined after having been excavated and evaluated as another step in thevalidation process (step 109).

Another embodiment of the present invention (see e.g. FIG. 2) embodiesthe present invention on a computer readable memory (or having hardwareconfigured as such) executable by a computer, will instruct theprocessor (not shown) to collect the T-MFL data (step 201) such asdiscussed in relation to FIG. 1. The processor will then display thedata on a display (step 203), such as, for example, an LCD computerscreen. The analyst can utilize the various display options of thedisplay software to manually determine the optimal display settings forthe T-MFL data during analysis (step 205).

When determining the optimal display parameters (step 205), the analystselects various locations throughout the survey to determine one or moreC-Scan and A-Scan display settings for optimum identification of linearERW anomalies. One or more settings may be required for each wallthickness present in the pipeline. In doing so, the analyst can adjustthe T-MFL display contrast and color intensity until there is a highcontrast between the weld line within the T-MFL data 32 and thesurrounding display background. For example, as illustrated in FIGS. 2 band 10, display 30 displays T-MFL data 32 having a strong contrast 34between T-MFL data 32 and the surrounding background of the display.T-MFL data 32 also includes an anomaly signal 35 having red and yellow(color not shown) contrasting colors with a “bloom” rising above andbelow the primary T-MFL signal. As will be discussed later, the bloom isan extension of the anomaly signal 35 that extends into the adjacentchannels.

Different pipelines, for example, may require unique software or programproduct display values due to differences in magnetic characteristicsand due to diameter, wall thickness, grade, and tool speed during theactual inspection. The display software utilized as part of embodimentsof the present invention, for example, can have an icon, button, know orother user interface on the display that, when clicked or operated,automatically sets the display values for optimum contrast. Theprocessor then analyzes all the displayed data and responsively adjuststhe values. This responsive setting, however, produces varying resultsdepending on the amount of data displayed on the screen (i.e., how longa section of pipe and how much of the pipe circumference is displayed).

As illustrated at step 205 of FIG. 2 a, another display screen showinghow the gain and offset of the T-MFL signal, can be manually sized togenerate a high contrast display as part of the optimal displaysettings. The gain and offset values (or other variables depending onILI vendor software) can be dependent on the specific response of thepipeline in question, and can vary substantially from one pipeline tothe next. Once the presentation of the display has been optimized, theC-Scan/A-Scan values used for gain and offset (i.e., optimized settingvalues) can be saved, for example, with a unique file name, or otherwiserecorded for future reference. Note that FIG. 12 is a coloredillustration providing an example of incorrect display settingsresulting in a poor, fuzzy display with no clear indication of a bloomextending radially above and below a potential anomaly. FIG. 13 is acolored illustration providing an example of optimum display settingsresulting in a clear and distinct contrasting signal with a bloom 40extending radially above and below a clearly distinctive anomaly 35.

Embodiments of the present invention also allow the length of pipelinedata displayed (as T-MFL data 32) to be set. As illustrated in FIGS. 2 dand 8, in a preferred configuration, the display 30, for example, candisplay 20 feet of pipeline data at a time. This display distance,however, can be adjusted to any desired length. For example, in FIG. 2e, the range has been adjusted to 500 feet. Bear in mind that this largevalue increases the time for the computer to load the T-MFL data,therefore memory and processor speed can determine the optimum number toselect. The more data loaded at a time will increase the distance thatcan be reviewed before more data needs to be loaded.

In step 205 of FIG. 2 a, for example, in addition to length of pipedisplayed on display 30, the amount of pipe circumference displayed isimportant. The combination of length and percent of pipe circumferencedisplayed, for example, sets the aspect ratio for the displayedanomalies. For reasonably fast scans and low probability of missing animportant candidate anomaly, a preferred configuration displays no morethan 90 degrees of the pipeline circumference on display 30. This meansthat one must stop at the end of each pipe joint to “center” thelongitudinal seam in the next joint. This is best accomplished by“toggling on” the Angular display 38 of FIG. 2 f, which illustrates anexemplary embodiment of a display screen showing pipeline circumference36. Display 30, for example, may also include an icon which allows auser to “toggle” the display on and off. The quadrant section 39displayed in the outer ring can be rotated along its o'clock position,for example, by left-clicking the highlighted area of the outer ring anddragging it around for centering of the weld.

In step 207 of FIG. 2 a, once all display settings have been set up andfinalized, program products of the present invention scan T-MFL data 32using these values. During the scanning process, display softwareutilizes the saved C-Scan display settings (discussed above), and zoomsas necessary, to review anomaly 35 as shown in FIGS. 2 b and 10. Suchscanning may be conducted manually or responsively to the displaysoftware.

At step 207, for example, the C-Scan scrolling process is used to lookfor a, e.g., red anomaly pattern with a yellow transition ring centeredand contained within the green weld of display 30. In a preferredconfiguration, the background of the display will be colored green toblue in order to further magnify the contrasting nature of the display,Please note, however, that any color pallet may be utilized according toembodiments of the present invention. These contrasting anomalies areconsidered to be level one candidates. FIG. 9 is a color illustration ofsuch a display 30 having a level one candidate. FIG. 17 is a coloredillustration providing an example of an anomaly signal pattern highlyindicative of a crack-like anomaly.

If the anomaly fails to meet the characteristics of this first anomalylevel, the anomaly will be ignored and the process will continuecollecting data at step 201 of FIG. 2 a. FIG. 18 is a coloredillustration providing examples of anomaly signals for metal loss orother volumetric anomalies that extend across (transverse to) the seamof the longitudinal weld, and thus, are candidates for exclusion frombeing considered a candidate level one anomaly.

As illustrated in FIGS. 3 and 9, however, those level one candidatesthat have an associated bloom 40 and a pattern that is somewhatsymmetrical when cut through either the axial or transverse axis (orboth), are considered to be level two candidates (step 209). FIG. 14provides a colored illustration showing the extent of the bloom 40 ofthe anomaly 35 extending into adjacent channels. FIG. 15 is a coloredillustration showing a high contrast anomaly signal of a crack-likeanomaly 35 in the presence of trim issues illustrated as yellow linesextending longitudinally on either side of the anomaly 35.

If the anomaly fails to meet the characteristics of this second anomalylevel, the anomaly will be ignored and the process will continuecollecting data (step 201 of FIG. 2 a). FIG. 16 is a coloredillustration providing examples of an anomaly signal patternrepresenting trim issues. Particularly, the illustration shows the lackof an anomaly bloom 40, excessive length, and lack of symmetryindicative of trim issues.

When a level two candidate is found, the scrolling process can bestopped and the anomaly evaluated in greater detail. The zoom functioncan be used as necessary to evaluate the anomaly signal characteristics.FIG. 10 provides a color illustration showing a strong contrast betweenT-MFL data 32 and the surrounding background of the display 30 andshowing an anomaly signal 35 having red and yellow contrasting colorswith a bloom 40 rising above and below the primary T-MFL signal.

After review of the level two candidate is complete, the level twocandidate is analyzed to determine if it qualifies as a level threecandidate anomaly (step 211). If the anomaly length is less than thecritical flaw length discussed previously, the anomaly qualifies as alevel three candidate. As illustrated in FIGS. 2 b, 10, 4, and 11, inorder to measure the length of anomaly 35, the Measuring Window 42 of adisplay can be toggled on, for example, via icon 43. Once the measuringwindow is turned on, for example, measurement of the length of anomaly35 is simply a matter of setting up the display to enlarge the anomalyto reduce error and then dragging box 44 to anomaly 35. A good anomalydisplay for most situations, for example, is a 1:5 display and onlyshowing approximately one hour of the pipeline circumference. The sidesof box 44 can be adjusted by dragging them to the extremities of anomaly35 and the length can be read near the lower left hand corner as shownin FIG. 4. If the anomaly fails to meet the characteristics of thisthird anomaly level, the anomaly will be ignored and the process willcontinue collecting data (step 201 of FIG. 2 a). FIG. 11 is a coloredillustration showing utilization of the box 44 to measure the size ofthe anomaly 35, enlargement of the image of the anomaly 35 to reduceerror in the measurement, identification of extremities of the anomaly35 utilizing the color contrast between the red anomaly pattern andyellow transition ring, and accurate positioning of the box 44 forautomated measurement of the length of the anomaly 35. FIG. 11 alsoillustrates accurate sizing of the circumference of the longitudinalweld (e.g., selecting one hour of the circumference) throughidentification of the contrast between the green weld and the light bluebackground.

If, however, the anomaly does meet the characteristics of the thirdanomaly level, the anomaly will be further analyzed (step 213) todetermine if the anomaly is consistent with a level four candidateanomaly. To determine if the pattern is a level four candidate, theanomaly is reviewed using the A-Scan display, zooming as necessary andmanipulating the gain to increase the amplitude of the signal. FIG. 5illustrates an example of an A-Scan signal pattern having three anomalychannels. The fourth anomaly level identifiers are a smooth waveformthat stands out from surrounding background signals, an anomaly signalthat is not associated with a long area of ragged erratic signals (whichis typically indicative of trim issues as shown in FIGS. 6 and 7) and amaximum number of 4 anomaly channels, but typically 2 to 3 channels.Also, another key identifier of a level four anomaly is that when thegain of the anomaly is increased the anomaly will stand out from othersignals, defined as the “rise” of the signal.

FIG. 5 also illustrates an exemplary A-scan signal showing the smoothwaveform having only three affected channels. Each line in FIG. 5represents an T-MFL signal received from a sensor surrounding thecircumference of the pipeline. The y-axis represents the signalamplitude, while the x-axis represents the distance along the pipeline.As such, FIG. 5 illustrates three anomaly channels at a specifieddistance “D.” The same axis definitions apply to FIGS. 6 and 7.

If the anomaly meets the above criteria, for example, it becomes a levelfour candidate. The appropriate information is entered into a summaryspreadsheet for further review and final confirmation. The informationis included in a table, chart, or spreadsheet, such as, for example, anExcel Spreadsheet that lists: (1) joint number containing the anomaly(U/S girth weld number); (2) absolute distance to UIS girth weld; (3)absolute surrey distance to the anomaly; (4) o'clock position of theweld; and (5) classification categories to be one of the following: (a)“A”—anomaly signal exhibits all of the subject characteristics, (b)“B”—anomaly signal exhibits some of the subject characteristics, butthere is a question about its validity as an anomaly, (c) “C”—anomalysignal exhibits only a few of the subject characteristics but there areindications that it potentially could be an anomaly for considerationfor other stated reasons or (d) “D”—anomalies that were excluded fromthe process. The reasons for the exclusion can be noted in adocumentation spreadsheet; for example longer than determined criticallength. The summary spread sheet may also include the length of thepotential anomaly, any descriptions or comments and reasons whyanomalies were included/excluded from anomaly conclusion. Once theanomaly is included or excluded the display is returned to the C-Scanview and the scrolling continued.

It is important to remain informed of anomalies that fall into “B” or“C” categories so the process can be refined to only include “A” anomalysignals and a limited number of the other categories in the finalreport. Moreover, for each “A”, “B”, or “C” anomaly reported, theanalyst may provide screen captures as follows, having all screencaptures in PEG image format, for example, (hard copy in a book pluselectronic copy on DVD):

-   -   1. Full Screen of C-Scan in optimum display mode with display        set to 20 feet, 3 hours, and with centered. Include a girth weld        if possible.    -   2. Full Screen of C-Scan in optimum display mode with display        set to 5 feet, 1 hour, and with centered. Measurement Display is        on and properly positioned to show measured length legibly.    -   3. Full Screen of A-Scan in optimum display mode with display        set to 20 feet, 3 hours, and with centered. Gain should be        increased until “noise” comes up equally with “signal” as gain        is increased. Include a girth weld if possible.    -   4. Full Screen of A-Scan in optimum display mode with display        set to 5 feet, as little circumference as possible, and with        centered. Gain should be increased until “noise” comes up        equally with “signal” as gain is increased.

Once the “A”, “B”, or “C” anomalies have been reviewed and confirmed, acomplete dig sheet package can be generated utilizing embodiments ofprogram product, e.g., software, if required to validate the calloutsand develop an excavation/repair program. This listing can include alisting of each anomaly based upon the presence of the signalcharacteristics corresponding to Level four anomalies (i.e., “A”, “B”,“C” or “D” level anomalies).

Upon confirmation (step 215), embodiments of program product can createa list of all level five candidates (step 217). In other words, afterLevels one dim four have excluded the non-qualifying anomalies, thoseremaining are considered Level five anomalies. All level five candidatescan be investigated further by exposing the pipe and performing acomplete NDE examination. As such, from time to time it may becomenecessary to remove certain defects from the pipeline for additional andsometimes destructive metallurgical testing.

The present invention has numerous advantages. The T-MFL screeningprocess of the present invention can locate anomalies, such as cracks,in pipelines that otherwise go undetected by existing methods. If leftundetected, these cracks would typically fail in “rupture mode,” therebyleading to large volumes of contamination in the surrounding area and ishereby incorporated by reference in its entirety.

This application is a continuation and claims priority and benefit toU.S. Serial No. 12/270,432, filed on Nov. 13, 2008, which claimspriority and benefit to U.S. Provisional Application Ser. No.61/008,822, filed Dec. 21, 2007, and both of which are herebyincorporated herein by reference in their entireties. This applicationalso is a continuation of and claims priority and benefit to U.S. Ser.Nos. 12/949,896 and 12/950,118 each filed on Nov. 19, 2010, and whichlikewise each claim priority to U.S. Ser. No. 12/279,432 and U.S.Provisional Application Ser. No. 61/008,822, and all of which are herebyincorporated herein by reference in their entireties.

It is to be understood by those skilled in the art that the invention isnot limited to the exact details of construction, operation, exactmaterials, or embodiments shown and described, as modifications andequivalents will be apparent to one skilled in the art. For example,although discussed as steps in a computerized process, steps of thepresent invention may also be accomplished manually. In addition,although aspects of the present invention have been described withrespect to a computer, a computer device, a computer system, orprocessor executing program product or software that directs thefunctions of embodiments of the present invention, it should beunderstood by those skilled in the art that the present invention can beimplemented as a program product for use with various types of dataprocessing systems as well. Programs defining the functions ofembodiments of the present invention, for example, can be delivered to adata processing system via a variety of signal-bearing media, whichinclude, without limitation, non-rewritable storage media (e.g., CD-ROM,DVD-ROM), rewritable storage media (e.g., floppy disks, hard drivedisks, CD-R, or rewritable ROM media), and communication media, such asdigital and analog networks. It should be understood, therefore, thatsuch signal-bearing media, when carrying or embodying computer readableinstructions that direct the functions of embodiments of the presentinvention, represent alternative embodiments of the present invention.In the drawings and specification, there have been disclosedillustrative embodiments of the invention and, although specific termsare employed, they are used in a generic and descriptive sense only andnot for the purpose of limitation. Accordingly, the invention istherefore to be limited only by the scope of the appended claims.

1. A computer-implemented method to detect longitudinal-seam anomaliesin a pipeline weld of a pipeline, the computer-implemented methodcomprising: (a) receiving magnetic flux leakage data by a computer, themagnetic flux leakage data being representative of potential crack-likedefects in a longitudinal pipeline weld; (b) displaying the magneticflux leakage data on a display associated with the computer; (c)analyzing the magnetic flux leakage data by use of the computerassociated with the display responsive to signal characteristics andcritical flaw dimensions of the magnetic flux leakage data that may beindicators of the potential crack-like defects, the critical flawdimensions including at least a maximum length of the potentialcrack-like defects; and (d) determining potential crack-like defects byuse of the computer and associated display responsive to the analyzingof the magnetic flux leakage data.
 2. A computer-implemented method asdefined in claim 1, wherein the step of analyzing comprises determiningoptimal display settings on the display, the display comprising adisplay screen and the optimal display settings being responsive todifferent magnetic flux leakage characteristics, the determining of theoptimal display settings on the display screen including changing a gainand offset of the display screen in order to achieve a high contrastdisplay so that the presence of the potential crack-like defects readilymay be perceived on the display screen.
 3. A computer-implemented methodas defined in claim 1, wherein the step of determining potentialcrack-like defects including listing data representative of potentialcrack-like defects responsive to the signal characteristics and criticalflaw dimensions.
 4. A computer-implemented method as defined in claim 2,wherein determining the optimal display settings on the display furthercomprises: changing a length of data being displayed on the displayscreen; and changing an amount of data representing a pipelinecircumference begin displayed on the display screen, the amount of datarepresenting no more than ninety degrees of the pipeline circumference.5. A computer-implemented method as defined in claim 1, wherein thepipeline is comprised of steel and the potential crack-like defects arecrack air gaps, the crack air gaps being a function of crack length anddepth, steel properties and hoop stress.
 6. A computer-implementedmethod as defined in claim 1, further comprising the step of determininga validation dig list containing one or more crack-like defects by useof the computer, whereby the list is used to identify areas along thepipeline to be visually inspected for defects.
 7. A computer-implementedmethod as defined in claim 2, wherein the signal characteristicscomprise one or more of the following: (1) a smooth waveform; (2) lessthan four anomaly channels; (3) a non-erratic pattern; and (4) beingdistinguished from other signals on the display screen when a gain ofthe pattern of magnetic flux leakage data increases.
 8. Acomputer-implemented method defined in claim 2, wherein the analyzing ofthe magnetic flux data performed by the computer further comprises: (1)determining whether the magnetic flux leakage data is consistent with afirst predetermined anomaly level by use of the computer and responsiveto the optimal display settings to thereby define a level one anomalycandidate, the first predetermined anomaly level having first selectedsignal characteristics consistent with the presence of longitudinal-seamanomalies; (2) if the magnetic flux leakage data is consistent with thelevel one anomaly candidate, then determining if the level one anomalycandidate is consistent with a second predetermined anomaly level by useof the computer and responsive to the optimal display settings, thesecond predetermined anomaly level defining a level two anomalycandidate having second selected signal characteristics different thanthe first selected signal characteristics and still consistent with thepresence of longitudinal-seam anomalies; (3) if the level one anomalycandidate is consistent with the level two anomaly candidate, thendetermining if the level two anomaly candidate is consistent with athird predetermined anomaly level by use of the computer and responsiveto the optimal display settings, the third predetermined anomaly leveldefining a level three anomaly candidate having third selected signalcharacteristics different than the first and second selected signalcharacteristics and yet still consistent with the presence oflongitudinal-seam anomalies, the third selected signal characteristicsalso being indicative of the presence of the selected critical flawdimensions of a pipeline weld; and (4) if the level two anomalycandidate is consistent with the third level anomaly candidate, thendetermining if the level three anomaly candidate is consistent with afourth predetermined anomaly level by use of the computer and responsiveto the optimal display settings, the fourth predetermined anomaly leveldefining a level four anomaly candidate having fourth selected signalcharacteristics different than the first, second, and third selectedsignal characteristics and yet still consistent with the presence oflongitudinal-seam anomalies.
 9. A computer-implemented method to detectlongitudinal-seam anomalies in a pipeline weld of a pipeline, thecomputer-implemented method comprising: (a) receiving magnetic fluxleakage data by a computer, the magnetic flux leakage datarepresentative of potential crack-like defects in a longitudinalpipeline weld; (b) displaying the magnetic flux leakage data on adisplay connected to the computer; (c) analyzing the magnetic fluxleakage data by use of the computer and responsive to signalcharacteristics and critical flaw dimensions of the magnetic fluxleakage data that may be indicators of the potential crack-like defects,the critical flaw dimensions including at least a maximum length of thepotential crack-like defects, the analyzing of the magnetic flux dataperformed by the computer including: (1) determining whether themagnetic flux leakage data is consistent with a first predeterminedanomaly level by use of the computer to thereby define a level oneanomaly candidate, the first predetermined anomaly level having firstselected signal characteristics consistent with the presence oflongitudinal-seam anomalies, (2) if the magnetic flux leakage data isconsistent with the level one anomaly candidate, then determining if thelevel one anomaly candidate is consistent with a second predeterminedanomaly level by use of the computer, the second predetermined anomalylevel defining a level two anomaly candidate having second selectedsignal characteristics different than the first selected signalcharacteristics and still consistent with the presence oflongitudinal-seam anomalies, (3) if the level one anomaly candidate isconsistent with the level two anomaly candidate, then determining if thelevel two anomaly candidate is consistent with a third predeterminedanomaly level by use of the computer, the third predetermined anomalylevel defining a level three anomaly candidate having third selectedsignal characteristics different than the first and second selectedsignal characteristics and yet still consistent with the presence oflongitudinal-seam anomalies, the third selected signal characteristicsalso being indicative of the presence of the selected critical flawdimensions of a pipeline weld, and (4) if the level two anomalycandidate is consistent with the third level anomaly candidate, thendetermining if the level three anomaly candidate is consistent with afourth predetermined anomaly level by use of the computer, the fourthpredetermined anomaly level defining a level four anomaly candidatehaving fourth selected signal characteristics different than the first,second, and third selected signal characteristics and yet stillconsistent with the presence of longitudinal-seam anomalies; and (d)determining potential crack-like defects by use of the computer andassociated display responsive to the analyzing of the magnetic fluxleakage data.
 10. A computer-implemented method as defined in claim 9,wherein the step of analyzing comprises determining optimal displaysettings on the display, the display comprising a display screen and theoptimal display settings being responsive to different magnetic fluxleakage characteristics, the determining of the optimal display settingson the display screen including changing a gain and offset of thedisplay screen in order to achieve a high contrast display so that thepresence of the potential crack-like defects readily may be perceived onthe display screen.
 11. A computer-implemented method as defined inclaim 9, wherein the step of determining potential crack-like defectsincluding listing data representative of potential crack-like defectsresponsive to the signal characteristics and critical flaw dimensions.12. A computer-implemented method as defined in claim 11, whereindetermining the optimal display settings on the display furthercomprises: changing a length of data being displayed on the displayscreen; and changing an amount of data representing a pipelinecircumference begin displayed on the display screen, the amount of datarepresenting no more than ninety degrees of the pipeline circumference.13. A computer-implemented method as defined in claim 12, wherein thepipeline is comprised of steel and the potential crack-like defects arecrack air gaps, the crack air gaps being a function of crack length anddepth, steel properties and hoop stress.
 14. A computer-implementedmethod as defined in claim 13, further comprising the step ofdetermining a validation dig list containing one or more crack-likedefects by use of the computer, whereby the list is used to identifyareas along the pipeline to be visually inspected for defects.
 15. Acomputer-implemented method as defined in claim 14, wherein the signalcharacteristics comprise one or more of the following: (1) a smoothwaveform; (2) less than four anomaly channels; (3) a non-erraticpattern; and (4) being distinguished from other signals on the displayscreen when a gain of the pattern of magnetic flux leakage dataincreases.
 16. A computer-implement method to detect longitudinal-seamanomalies in a low frequency pipeline weld of a pipeline, thecomputer-implemented method comprising: receiving magnetic flux leakagedata related to one or more anomaly air gaps within a longitudinalpipeline weld by a computer; displaying on a display associated with thecomputer the magnetic flux leakage data as one or more selected patternsof data representing selected signal characteristics of the pipelineweld; and analyzing the magnetic flux leakage data responsive to theselected signal characteristic and selected critical flaw dimensions ofthe pipeline weld, the selected signal characteristics being indicatorsof potential longitudinal-seam anomalies in the pipeline weld and theselected critical flaw dimensions being at least a maximum length of thepotential longitudinal-seam anomalies along a pattern of magnetic fluxleakage data.
 17. A computer-implemented method as defined in claim 16,wherein the analyzing of the magnetic flux data performed by thecomputer comprises: determining optimal display settings on the display,the optimal display settings being responsive to different magnetic fluxleakage characteristics; and determining whether the magnetic fluxleakage data is consistent with a first predetermined anomaly levelresponsive to the optimal display settings to thereby define a level oneanomaly candidate, the first predetermined anomaly level having firstselected signal characteristics consistent with the presence oflongitudinal-seam anomalies.
 18. A computer-implemented method asdefined in claim 17, wherein the analyzing of the magnetic flux dataperformed by the computer further comprises if the magnetic flux leakagedata is consistent with the level one anomaly candidate, thendetermining if the level one anomaly candidate is consistent with asecond predetermined anomaly level responsive to the optimal displaysettings, the second predetermined anomaly level defining a level twoanomaly candidate having second selected signal characteristicsdifferent than the first selected signal characteristics and stillconsistent with the presence of longitudinal-seam anomalies.
 19. Acomputer-implemented method as defined in claim 18, wherein theanalyzing of the magnetic flux data performed by the computer furthercomprises if the level one anomaly candidate is consistent with thelevel two anomaly candidate, then determining if the level two anomalycandidate is consistent with a third predetermined anomaly levelresponsive to the optimal display settings, the third predeterminedanomaly level defining a level three anomaly candidate having thirdselected signal characteristics different than the first and secondselected signal characteristics and yet still consistent with thepresence of longitudinal-seam anomalies, the third selected signalcharacteristics also being indicative of the presence of the selectedcritical flaw dimensions of a pipeline weld.
 20. A computer-implementedmethod as defined in claim 19, wherein the analyzing of the magneticflux data performed by the computer further comprises: if the level twoanomaly candidate is consistent with the third level anomaly candidate,then determining if the level three anomaly candidate is consistent witha fourth predetermined anomaly level responsive to the optimal displaysettings, the fourth predetermined anomaly level defining a level fouranomaly candidate having fourth selected signal characteristicsdifferent than the first, second, and third selected signalcharacteristics and yet still consistent with the presence oflongitudinal-seam anomalies.
 21. A computer-implemented method asdefined in claim 20, wherein, if the level three anomaly candidate isconsistent with the level four anomaly candidate, then generating anoutput of the level four candidate anomaly to further determine if thelevel four anomaly candidate is consistent with a fifth predeterminedanomaly level, the fifth predetermined anomaly level defining a levelfive anomaly candidate having fifth selected signal characteristicsdifferent from the first, second, third and fourth selected signalcharacteristics and yet still being indicative of one or morelongitudinal-seam anomalies in the pipeline weld potentially leading tofluid leakage within the pipeline when the pipeline contains fluidtherein.
 22. A computer-implemented method as defined in claim 21,wherein the first selected signal characteristics defining the level oneanomaly candidate comprises a pattern of data representing the level onecandidate anomaly having contrasting colors associated therewith.
 23. Acomputer-implemented method as defined in claim 22, wherein the secondselected signal characteristics defining the level two anomaly candidatecomprises a pattern of data representing the level two anomaly candidatehaving a symmetrical pattern and one or more associated blooms.
 24. Acomputer-implemented method as defined in claim 23, wherein the thirdselected signal characteristics defining the level three anomalycandidate comprises a pattern of data representing the level threeanomaly candidate having an anomaly length, along an axis of the patternof data, which is less than a maximum length defined within the selectedcritical flaw dimensions.
 25. A computer-implemented method as definedin claim 24, wherein the level four anomaly candidate comprises apattern of data having one or more of the selected signalcharacteristics when displayed on the display, the selected signalcharacteristics comprising the following: (1) a smooth waveform; (2)less than four anomaly channels; (3) a non-erratic pattern; or (4) beingdistinguished from other signals on the display when a gain of thepattern of data is increased.
 26. A computer-implemented method asdefined in claim 25, wherein the output of the level four anomalycandidate comprises a listing of each level four anomaly candidateresponsive to the presence of the fourth selected signal characteristicswithin the pattern of data representing the level four candidate anomaly